Health capabilities and diabetes self-management: The impact of economic, social, and cultural resources
Bibliographic record
Abstract
While the "social determinants of health" view compels us to explore how social structures shape health outcomes, it often ignores the role individual agency plays. In contrast, approaches that focus on individual choice and personal responsibility for health often overlook the influence of social structures. Amartya Sen's "capabilities" framework and its derivative the "health capabilities" (HC) approach attempts to accommodate both points of view, acknowledging that individuals function under social conditions over which they have little control, while also acting as agents in their own health and well-being. This paper explores how economic, social, and cultural resources shape the health capability of people with diabetes, focusing specifically on dietary practices. Health capability and agency are central to dietary practices, while also being shaped by immediate and broader social conditions that can generate habits and a lifestyle that constrain dietary behaviors. From January 2011 to December 2012, we interviewed 45 people with diabetes from a primary care clinic in Ontario (Canada) to examine how their economic, social, and cultural resources combine to influence dietary practices relative to their condition. We classified respondents into low, medium, and high resource groups based on economic circumstances, and compared how economic resources, social relationships, health-related knowledge and values combine to enhance or weaken health capability and dietary management. Economic, social, and cultural resources conspired to undermine dietary management among most in the low resource group, whereas social influences significantly influenced diet among many in the medium group. High resource respondents appeared most motivated to maintain a healthy diet, and also had the social and cultural resources to enable them to do so. Understanding the influence of all three types of resources is critical for constructing ways to enhance health capability, chronic disease self-management, and health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".